The action anticipation task refers to predicting what action will happen based on observed videos, which requires the model to have a strong ability to summarize the present and then reason about the future. Experience and common sense suggest that there ...

Egocentric action anticipation, as the task to infer future actions based on egocentric observations, is very appealing to discover patterns, conducts and, eventually, action causality. In the real world, the observations are usually uncertain, ...

Anticipating human intention from videos has broad applications, such as automatic driving, robot assistive technology, and virtual reality. This study addresses the problem of intention action anticipation using egocentric video sequences to ...

Dense action anticipation, as opposed to next action anticipation, deals with predicting multiple actions over a long horizon of a few minutes. Recent approaches for dense action anticipation are based on deep learning, which lacks ...

Egocentric action anticipation aims to predict the future actions the camera wearer will perform from the observation of the past. While predictions about the future should be available before the predicted events take place, most ...

• Current egocentric action anticipation evaluation schemes shown to be biased.
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This paper presents a comprehensive framework of datasets and algorithms for action recognition in scenarios where data is scarce, unstructured, and unscripted. The long-term objective of this work is an intelligent assistant to the medic, a ...

Action anticipation involves predicting future actions having observed the initial portion of a video. Typically, the observed video is processed as a whole to obtain a video-level representation of the ongoing activity in the video, which is then ...

Streaming video recognition reasons about objects and their actions in every frame of a video. A good streaming recognition model captures both long-term dynamics and short-term changes of video. Unfortunately, in most existing methods, the ...$\mathrm{}$

To meet the demand for powerful models for practical applications in real time, the focus of research on human actions has shifted from offline detection to online and real-time understanding, such as driver-assistance systems, surveillance ...

Egocentric action anticipation consists in predicting a future action the camera wearer will perform from egocentric video. While the task has recently attracted the attention of the research community, current approaches assume that the input ...

• We introduce a novel velocity-to-velocity learning paradigm for human motion prediction, and propose different architectures to implement this paradigm.

Forecasting human motion from a sequence of human poses is an important problem in the fields of computer vision and robotics. Most previous approaches merely consider learning the temporal dynamics of body joints or joint angles, ...

Future prediction, especially in long-range videos, requires reasoning from current and past observations. In this work, we address questions of temporal extent, scaling, and level of semantic abstraction with a flexible multi-granular temporal ...

We address the challenging task of anticipating human-object interaction in first person videos. Most existing methods either ignore how the camera wearer interacts with objects, or simply considers body motion as a separate modality. In contrast, ...

Current action anticipation approaches often neglect the intrinsic uncertainty of future predictions when loss functions or evaluation measures are designed. The uncertainty of future observations is especially relevant in the context of ...

Cognitive neuroscience experiments show how people intensify the exchange of non-verbal cues when they work on a joint task towards a common goal. When individuals share their intentions, it creates a social interaction that drives the mutual ...